
#importing libraries for our purpose
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df=pd.read_csv('netflix_titles.csv')


#length of data
len(df)

8807


df.head(10)


#checking datatypes
df.dtypes

show_id         object
type            object
title           object
director        object
cast            object
country         object
date_added      object
release_year     int64
rating          object
duration        object
listed_in       object
description     object
dtype: object


#number of unique values in our data
for i in df.columns:
  print(i,':',df[i].nunique())

show_id : 8807
type : 2
title : 8807
director : 4528
cast : 7692
country : 748
date_added : 1767
release_year : 74
rating : 17
duration : 220
listed_in : 514
description : 8775


#checking null values in every column of our data
df.isnull().sum()/len(df)*100

show_id          0.000000
type             0.000000
title            0.000000
director        29.908028
cast             9.367549
country          9.435676
date_added       0.113546
release_year     0.000000
rating           0.045418
duration         0.034064
listed_in        0.000000
description      0.000000
dtype: float64


#checking the occurences of each of the ratings
df['rating'].value_counts()

TV-MA       3207
TV-14       2160
TV-PG        863
R            799
PG-13        490
TV-Y7        334
TV-Y         307
PG           287
TV-G         220
NR            80
G             41
TV-Y7-FV       6
NC-17          3
UR             3
74 min         1
84 min         1
66 min         1
Name: rating, dtype: int64


#unnesting the directors column, i.e- creating separate lines for each director in a movie
constraint1=df['director'].apply(lambda x: str(x).split(', ')).tolist()
df_new1=pd.DataFrame(constraint1,index=df['title'])
df_new1=df_new1.stack()
df_new1=pd.DataFrame(df_new1.reset_index())
df_new1.rename(columns={0:'Directors'},inplace=True)
df_new1.drop(['level_1'],axis=1,inplace=True)
df_new1.head()


#unnesting the cast column, i.e- creating separate lines for each cast member in a movie
constraint2=df['cast'].apply(lambda x: str(x).split(', ')).tolist()
df_new2=pd.DataFrame(constraint2,index=df['title'])
df_new2=df_new2.stack()
df_new2=pd.DataFrame(df_new2.reset_index())
df_new2.rename(columns={0:'Actors'},inplace=True)
df_new2.drop(['level_1'],axis=1,inplace=True)
df_new2.head()


#unnesting the listed_in column, i.e- creating separate lines for each genre in a movie
constraint3=df['listed_in'].apply(lambda x: str(x).split(', ')).tolist()
df_new3=pd.DataFrame(constraint3,index=df['title'])
df_new3=df_new3.stack()
df_new3=pd.DataFrame(df_new3.reset_index())
df_new3.rename(columns={0:'Genre'},inplace=True)
df_new3.drop(['level_1'],axis=1,inplace=True)
df_new3.head()


#unnesting the country column, i.e- creating separate lines for each country in a movie
constraint4=df['country'].apply(lambda x: str(x).split(', ')).tolist()
df_new4=pd.DataFrame(constraint4,index=df['title'])
df_new4=df_new4.stack()
df_new4=pd.DataFrame(df_new4.reset_index())
df_new4.rename(columns={0:'country'},inplace=True)
df_new4.drop(['level_1'],axis=1,inplace=True)
df_new4.head()


#merging the unnested director data with unnested actors data
df_new5=df_new2.merge(df_new1,on=['title'],how='inner')
#merging the above merged data with unnested genre data
df_new6=df_new5.merge(df_new3,on=['title'],how='inner')
#merging the above merged data with unnested country data
df_new=df_new6.merge(df_new4,on=['title'],how='inner')


#replacing nan values of director and actor by Unknown Actor and Director
df_new['Actors'].replace(['nan'],['Unknown Actor'],inplace=True)
df_new['Directors'].replace(['nan'],['Unknown Director'],inplace=True)
df_new['country'].replace(['nan'],[np.nan],inplace=True)
df_new.head()


#merging our unnested data with the original data
df_final=df_new.merge(df[['show_id', 'type', 'title', 'date_added',
       'release_year', 'rating', 'duration']],on=['title'],how='left')
df_final.head()


#now checking nulls
df_final.isnull().sum()

title               0
Actors              0
Directors           0
Genre               0
country         11897
show_id             0
type                0
date_added        158
release_year        0
rating             67
duration            3
dtype: int64


df_final.loc[df_final['duration'].isnull(),'duration']=df_final.loc[df_final['duration'].isnull(),'duration'].fillna(df_final['rating'])


df_final.loc[df_final['rating'].str.contains('min', na=False),'rating']='NR'


df_final.isnull().sum()

title               0
Actors              0
Directors           0
Genre               0
country         11897
show_id             0
type                0
date_added        158
release_year        0
rating             67
duration            0
dtype: int64


#Ratings can't be in min, so it has been made NR(i.e- Non Rated)
df_final.loc[df_final['rating'].str.contains('min', na=False),'rating']='NR'
df_final['rating'].fillna('NR',inplace=True)
pd.set_option('display.max_rows',None)


#just an attempt to observe nulls in date_added column
df_final[df_final['date_added'].isnull()].head()


#date added column is imputed on the basis of release year,i.e- suppose there's a null for date_added 
#when release year was 2013.So below piece of code just checks the mode of date added for release year=2013
# and imputes in place of nulls the corresponding mode

for i in df_final[df_final['date_added'].isnull()]['release_year'].unique():
  imp=df_final[df_final['release_year']==i]['date_added'].mode().values[0]

  df_final.loc[df_final['release_year']==i,'date_added']=
    df_final.loc[df_final['release_year']==i,'date_added'].fillna(imp)


#country column is imputed on the basis of director,i.e- suppose there's a null for country 
#when we have a director whose other movies have a country given.So below piece of code just checks the mode of 
#country for the director
# and imputes in place of nulls the corresponding mode

for i in df_final[df_final['country'].isnull()]['Directors'].unique():
  if i in df_final[~df_final['country'].isnull()]['Directors'].unique():
    imp=df_final[df_final['Directors']==i]['country'].mode().values[0]
    df_final.loc[df_final['Directors']==i,'country']=df_final.loc[df_final['Directors']==i,'country'].fillna(imp)


for i in df_final[df_final['country'].isnull()]['Actors'].unique():
  if i in df_final[~df_final['country'].isnull()]['Actors'].unique():
    imp=df_final[df_final['Actors']==i]['country'].mode().values[0]
    df_final.loc[df_final['Actors']==i,'country']=df_final.loc[df_final['Actors']==i,'country'].fillna(imp)
#If there are still nulls, I just replace it by Unknown Country
df_final['country'].fillna('Unknown Country',inplace=True)       
df_final.isnull().sum()

title           0
Actors          0
Directors       0
Genre           0
country         0
show_id         0
type            0
date_added      0
release_year    0
rating          0
duration        0
dtype: int64


df_final['duration'].value_counts() #no. of rows

1 Season      35035
2 Seasons      9559
3 Seasons      5084
94 min         4343
106 min        4040
97 min         3624
95 min         3560
96 min         3484
93 min         3480
90 min         3305
105 min        3209
107 min        3103
101 min        3048
102 min        3017
103 min        2985
98 min         2984
99 min         2956
91 min         2915
92 min         2863
104 min        2822
88 min         2781
110 min        2711
100 min        2697
108 min        2614
112 min        2594
85 min         2486
89 min         2420
86 min         2213
4 Seasons      2134
116 min        2122
118 min        2119
119 min        2075
87 min         2063
109 min        2020
113 min        1990
120 min        1845
117 min        1770
121 min        1728
5 Seasons      1698
111 min        1667
124 min        1590
114 min        1529
127 min        1505
115 min        1444
123 min        1398
125 min        1299
122 min        1298
84 min         1268
128 min        1241
130 min        1216
126 min        1205
81 min         1203
83 min         1192
133 min        1169
137 min        1122
82 min         1100
136 min        1092
132 min        1047
131 min         913
135 min         851
7 Seasons       843
129 min         837
75 min          794
148 min         671
140 min         658
6 Seasons       633
79 min          629
139 min         617
143 min         608
80 min          586
134 min         572
145 min         549
149 min         540
138 min         540
74 min          517
78 min          506
141 min         495
72 min          470
142 min         464
46 min          451
77 min          447
150 min         442
172 min         432
158 min         424
76 min          408
73 min          408
151 min         395
147 min         379
163 min         371
154 min         356
146 min         342
162 min         333
54 min          323
144 min         303
153 min         300
71 min          297
70 min          289
8 Seasons       286
157 min         284
155 min         275
68 min          263
9 Seasons       257
24 min          252
161 min         230
166 min         228
10 Seasons      220
156 min         214
58 min          197
176 min         192
152 min         186
168 min         178
165 min         177
171 min         174
160 min         169
185 min         166
22 min          162
69 min          160
44 min          149
173 min         144
181 min         144
63 min          141
180 min         133
13 Seasons      132
159 min         132
26 min          128
170 min         120
177 min         117
23 min          116
60 min          114
64 min          113
28 min          113
12 Seasons      111
164 min         110
200 min         108
59 min          107
66 min          106
51 min          105
30 min          104
61 min          100
52 min           99
65 min           98
15 Seasons       96
62 min           92
33 min           91
25 min           86
47 min           81
187 min          78
182 min          76
42 min           74
67 min           74
56 min           73
48 min           73
186 min          72
57 min           71
40 min           68
179 min          66
32 min           64
27 min           62
224 min          60
208 min          60
29 min           60
53 min           57
55 min           56
205 min          54
174 min          53
192 min          51
201 min          48
45 min           48
209 min          40
229 min          40
195 min          36
169 min          34
50 min           34
190 min          34
36 min           33
11 Seasons       30
194 min          30
203 min          30
189 min          30
17 Seasons       30
204 min          29
214 min          27
21 min           26
35 min           25
38 min           25
193 min          24
228 min          24
178 min          24
13 min           23
14 min           23
212 min          21
253 min          21
15 min           20
167 min          20
233 min          18
237 min          18
49 min           16
37 min           16
43 min           16
312 min          15
12 min           14
31 min           13
191 min          13
230 min          12
41 min           11
19 min            8
273 min           7
34 min            6
17 min            5
39 min            5
10 min            4
16 min            4
196 min           4
20 min            4
18 min            4
3 min             4
5 min             3
11 min            2
8 min             2
9 min             2
Name: duration, dtype: int64


#removing mins from data
df_final['duration']=df_final['duration'].str.replace(" min","")
df_final.head()

#you can use split and get the first part


df_final['duration'].unique()

array(['90', '2 Seasons', '1 Season', '91', '125', '9 Seasons', '104',
       '127', '4 Seasons', '67', '94', '5 Seasons', '161', '61', '166',
       '147', '103', '97', '106', '111', '3 Seasons', '110', '105', '96',
       '124', '116', '98', '23', '115', '122', '99', '88', '100',
       '6 Seasons', '102', '93', '95', '85', '83', '113', '13', '182',
       '48', '145', '87', '92', '80', '117', '128', '119', '143', '114',
       '118', '108', '63', '121', '142', '154', '120', '82', '109', '101',
       '86', '229', '76', '89', '156', '112', '107', '129', '135', '136',
       '165', '150', '133', '70', '84', '140', '78', '7 Seasons', '64',
       '59', '139', '69', '148', '189', '141', '130', '138', '81', '132',
       '10 Seasons', '123', '65', '68', '66', '62', '74', '131', '39',
       '46', '38', '8 Seasons', '17 Seasons', '126', '155', '159', '137',
       '12', '273', '36', '34', '77', '60', '49', '58', '72', '204',
       '212', '25', '73', '29', '47', '32', '35', '71', '149', '33', '15',
       '54', '224', '162', '37', '75', '79', '55', '158', '164', '173',
       '181', '185', '21', '24', '51', '151', '42', '22', '134', '177',
       '13 Seasons', '52', '14', '53', '8', '57', '28', '50', '9', '26',
       '45', '171', '27', '44', '146', '20', '157', '17', '203', '41',
       '30', '194', '15 Seasons', '233', '237', '230', '195', '253',
       '152', '190', '160', '208', '180', '144', '5', '174', '170', '192',
       '209', '187', '172', '16', '186', '11', '193', '176', '56', '169',
       '40', '10', '3', '168', '312', '153', '214', '31', '163', '19',
       '12 Seasons', '179', '11 Seasons', '43', '200', '196', '167',
       '178', '228', '18', '205', '201', '191'], dtype=object)


df_final['duration_copy']=df_final['duration'].copy()
df_final1=df_final.copy()


df_final1.loc[df_final1['duration_copy'].str.contains('Season'),'duration_copy']=0
df_final1['duration_copy']=df_final1['duration_copy'].astype('int')
df_final1.head()

#you can use type as hue to segregate


df_final1['duration_copy'].describe()

count    201991.000000
mean         77.152789
std          52.269154
min           0.000000
25%           0.000000
50%          95.000000
75%         112.000000
max         312.000000
Name: duration_copy, dtype: float64


import seaborn as sns
sns.distplot(df_final1['duration_copy'], hist=True, kde=True, 
bins=int(36), color = 'darkblue', 
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 4})
plt.show()

/Users/suraaj/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


bins1 = [-1,1,50,80,100,120,150,200,315]
labels1 = ['<1','1-50','50-80','80-100','100-120','120-150','150-200','200-315']
df_final1['duration_copy'] = pd.cut(df_final1['duration_copy'],bins=bins1,labels=labels1)
df_final1.head()


df_final1.loc[~df_final1['duration'].str.contains('Season'),'duration']=df_final1.loc[~df_final1['duration'].str.contains('Season'),'duration_copy']
df_final1.drop(['duration_copy'],axis=1,inplace=True)
df_final1.head()


df_final1['duration'].value_counts()

80-100        52937
100-120       48724
1 Season      35035
120-150       26691
2 Seasons      9559
50-80          7700
150-200        6737
3 Seasons      5084
1-50           2530
4 Seasons      2134
5 Seasons      1698
7 Seasons       843
6 Seasons       633
200-315         524
8 Seasons       286
9 Seasons       257
10 Seasons      220
13 Seasons      132
12 Seasons      111
15 Seasons       96
17 Seasons       30
11 Seasons       30
Name: duration, dtype: int64


from datetime import datetime
from dateutil.parser import parse
arr=[]
for i in df_final1['date_added'].values:
  dt1=parse(i)
  arr.append(dt1.strftime('%Y-%m-%d'))
df_final1['Modified_Added_date'] =arr
df_final1['Modified_Added_date']=pd.to_datetime(df_final1['Modified_Added_date'])
df_final1['month_added']=df_final1['Modified_Added_date'].dt.month
df_final1['week_Added']=df_final1['Modified_Added_date'].dt.week
df_final1['year']=df_final1['Modified_Added_date'].dt.year
df_final1.head()

/var/folders/qc/486wrwtd4mj2pdzxt66k36c00000gn/T/ipykernel_38030/917954583.py:10: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.
  df_final1['week_Added']=df_final1['Modified_Added_date'].dt.week


#optional
#Titles such as Bahubali(Hindi Version),Bahubali(Telugu Version) were there. Since it's only one movie in different languages,
#presence of brackets and content between brackets is removed.
df_final1['title']=df_final1['title'].str.replace(r"\(.*\)","")
df_final1.head()

/var/folders/qc/486wrwtd4mj2pdzxt66k36c00000gn/T/ipykernel_38030/3753997727.py:3: FutureWarning: The default value of regex will change from True to False in a future version.
  df_final1['title']=df_final1['title'].str.replace(r"\(.*\)","")


#number of distinct titles on the basis of genre
df_final1.groupby(['Genre']).agg({"title":"nunique"}).sort_values(by=['title'], ascending=False)


df_genre=df_final1.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


#number of distinct titles on the basis of type
df_final1.groupby(['type']).agg({"title":"nunique"})


 df_type=df_final1.groupby(['type']).agg({"title":"nunique"}).reset_index()
 plt.pie(df_type['title'],explode=(0.05,0.05), labels=df_type['type'],colors=['purple','green'],autopct='%.1f%%')
 plt.show()


#number of distinct titles on the basis of country
df_final1.groupby(['country']).agg({"title":"nunique"})
#arrange by descending order


df_final1['country'] = df_final1['country'].str.replace(',', '')
df_final1.head()


#number of distinct titles on the basis of country
df_final1.groupby(['country']).agg({"title":"nunique"})


df_country=df_final1.groupby(['country']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:5]
plt.figure(figsize=(15,8))
plt.barh(df_country[::-1]['country'], df_country[::-1]['title'],color=['blue'])
plt.xlabel('Titles by Countries')
plt.ylabel('Countries')
plt.show()


#number of distinct titles on the basis of rating
df_final1.groupby(['rating']).agg({"title":"nunique"})


df_rating=df_final1.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


#number of distinct titles on the basis of duration
df_final1.groupby(['duration']).agg({"title":"nunique"})


df_duration=df_final1.groupby(['duration']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_duration[::-1]['duration'], df_duration[::-1]['title'],color=['pink'])
plt.xlabel('Frequency by Duration')
plt.ylabel('Duration')
plt.show()


#number of distinct titles on the basis of Actors
df_final1.groupby(['Actors']).agg({"title":"nunique"})


df_actors=df_final1.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:31]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actors')
plt.show()


#number of distinct titles on the basis of Actors
df_final1.groupby(['Directors']).agg({"title":"nunique"})


df_directors=df_final1.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:31]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


#number of distinct titles on the basis of year
df_final1.groupby(['year']).agg({"title":"nunique"})


df_year=df_final1.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


#number of distinct titles on the basis of week
df_final1.groupby(['week_Added']).agg({"title":"nunique"})


df_week=df_final1.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


#number of distinct titles on the basis of week
df_final1.groupby(['month_added']).agg({"title":"nunique"})


df_month=df_final1.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("Movies Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_final1[df_final1['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


df_shows=df_final1[df_final1['type']=='TV Show']
df_movies=df_final1[df_final1['type']=='Movie']


df_genre=df_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_genre=df_movies.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_country=df_shows.groupby(['country']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_country[::-1]['country'], df_country[::-1]['title'],color=['blue'])
plt.xlabel('Titles by Countries')
plt.ylabel('Countries')
plt.show()


df_country=df_movies.groupby(['country']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_country[::-1]['country'], df_country[::-1]['title'],color=['blue'])
plt.xlabel('Titles by Countries')
plt.ylabel('Countries')
plt.show()


df_rating=df_shows.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_rating=df_movies.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_duration=df_shows.groupby(['duration']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_duration[::-1]['duration'], df_duration[::-1]['title'],color=['pink'])
plt.xlabel('Frequency by Duration')
plt.ylabel('Duration')
plt.show()


df_duration=df_movies.groupby(['duration']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_duration[::-1]['duration'], df_duration[::-1]['title'],color=['pink'])
plt.xlabel('Frequency by Duration')
plt.ylabel('Duration')
plt.show()


df_actors=df_shows.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:31]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actors')
plt.show()


df_actors=df_movies.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:31]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actors')
plt.show()


df_directors=df_shows.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:31]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_directors=df_movies.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:31]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_year=df_shows.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Shows Released in the Year")
plt.xlabel("Year")
plt.show()


df_year=df_movies.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


df_week=df_shows.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_week=df_movies.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_month=df_shows.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("TV Shows Released in the Month")
plt.xlabel("Month")
plt.show()


df_month=df_movies.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("Movies Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_movies[df_movies['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


df_release_year=df_movies[df_movies['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


#below countries will be analyzed for both shows and movies
shows_and_movies=['United States','India','United Kingdom']
#below countries will be only analyzed on basis of shows
only_shows=['Japan','South Korea']


#Analyzing USA for both shows and movies
df_usa_shows=df_final1[df_final1['country']=='United States'][df_final1[df_final1['country']=='United States']['type']=='TV Show']
df_usa_movies=df_final1[df_final1['country']=='United States'][df_final1[df_final1['country']=='United States']['type']=='Movie']


df_genre=df_usa_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_genre=df_usa_movies.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_rating=df_usa_shows.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_rating=df_usa_movies.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_duration=df_usa_movies.groupby(['duration']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_duration[::-1]['duration'], df_duration[::-1]['title'],color=['pink'])
plt.xlabel('Frequency by Duration')
plt.ylabel('Duration')
plt.show()


df_actors=df_usa_shows.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actors')
plt.show()


df_actors=df_usa_movies.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actors')
plt.show()


df_directors=df_usa_shows.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_directors=df_usa_movies.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_year=df_usa_shows.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Shows Released in the Year")
plt.xlabel("Year")
plt.show()


df_year=df_usa_movies.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Shows Released in the Year")
plt.xlabel("Year")
plt.show()


df_week=df_usa_shows.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_week=df_usa_movies.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_month=df_usa_shows.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("TV Shows Released in the Month")
plt.xlabel("Month")
plt.show()


df_month=df_usa_movies.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("Movies Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_usa_movies[df_usa_movies['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


df_release_year=df_usa_shows[df_usa_shows['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


df_usa_movies.head()


#Analysing a combination of actors and directors
df_usa_movies['Actor_Director_Combination'] = df_usa_movies.Actors.str.cat(df_usa_movies.Directors, sep=' and ')
df_usa_movies_subset=df_usa_movies[df_usa_movies['Actors']!='Unknown Actor']
df_usa_movies_subset=df_usa_movies_subset[df_usa_movies_subset['Directors']!='Unknown Director']
df_usa_movies_subset.head()


df_usa_shows['Actor_Director_Combination'] = df_usa_shows.Actors.str.cat(df_usa_shows.Directors, sep=' and ')
df_usa_shows_subset=df_usa_shows[df_usa_shows['Actors']!='Unknown Actor']
df_usa_shows_subset=df_usa_shows_subset[df_usa_shows_subset['Directors']!='Unknown Director']
df_usa_shows_subset.head()


df_actors_directors=df_usa_shows_subset.groupby(['Actor_Director_Combination']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_actors_directors[::-1]['Actor_Director_Combination'], df_actors_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actor-Director Combination')
plt.show()


df_actors_directors=df_usa_movies_subset.groupby(['Actor_Director_Combination']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:20]
plt.figure(figsize=(15,8))
plt.barh(df_actors_directors[::-1]['Actor_Director_Combination'], df_actors_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actor-Director Combination')
plt.show()


df_actors_directors[::-1]['Actor_Director_Combination'].values

array(['Rory Markham and Mike Gunther', 'Erin Mathews and Steve Ball',
       'Danny Trejo and Robert Rodriguez',
       'Jeff Dunham and Michael Simon', 'Smith Foreman and Stanley Moore',
       'Marlon Wayans and Michael Tiddes', 'Adam Sandler and Steve Brill',
       'Maisie Benson and Stanley Moore', 'Ashleigh Ball and Ishi Rudell',
       'Tara Strong and Ishi Rudell', 'Rebecca Shoichet and Ishi Rudell',
       'Kerry Gudjohnsen and Alex Woo',
       'Kerry Gudjohnsen and Stanley Moore', 'Paul Killam and Alex Woo',
       'Paul Killam and Stanley Moore', 'Andrea Libman and Ishi Rudell',
       'Kevin Hart and Leslie Small', 'Maisie Benson and Alex Woo',
       'Alexa PenaVega and Robert Rodriguez',
       'Tabitha St. Germain and Ishi Rudell'], dtype=object)


#Analyzing India for both shows and movies
df_india_shows=df_final1[df_final1['country']=='India'][df_final1[df_final1['country']=='India']['type']=='TV Show']
df_india_movies=df_final1[df_final1['country']=='India'][df_final1[df_final1['country']=='India']['type']=='Movie']


df_genre=df_india_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_genre=df_india_movies.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_rating=df_india_shows.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_rating=df_india_movies.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_duration=df_india_movies.groupby(['duration']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_duration[::-1]['duration'], df_duration[::-1]['title'],color=['pink'])
plt.xlabel('Frequency by Duration')
plt.ylabel('Duration')
plt.show()


df_actors=df_india_shows.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actors')
plt.show()


df_actors['Actors'].values

array(['Rajesh Kava', 'Nishka Raheja', 'Prakash Raj', 'Sabina Malik',
       'Anjali', 'Aranya Kaur', 'Sonal Kaushal', 'Chandan Anand',
       'Danish Husain'], dtype=object)


df_actors=df_india_movies.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actors')
plt.show()


df_actors['Actors'].values

array(['Anupam Kher', 'Shah Rukh Khan', 'Naseeruddin Shah',
       'Akshay Kumar', 'Om Puri', 'Amitabh Bachchan', 'Julie Tejwani',
       'Paresh Rawal', 'Boman Irani', 'Rupa Bhimani', 'Kareena Kapoor',
       'Ajay Devgn', 'Rajesh Kava', 'Kay Kay Menon'], dtype=object)


df_directors=df_india_shows.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_directors['Directors'].values

array(['Gautham Vasudev Menon', 'Abhishek Chaubey', 'Sudha Kongara',
       'Rathindran R Prasad', 'Sankalp Reddy', 'Sarjun',
       'Soumendra Padhi', 'Srijit Mukherji', 'Tharun Bhascker Dhaassyam'],
      dtype=object)


df_directors=df_india_movies.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_directors['Directors'].values

array(['Rajiv Chilaka', 'Suhas Kadav', 'David Dhawan', 'Anurag Kashyap',
       'Umesh Mehra', 'Ram Gopal Varma', 'Dibakar Banerjee',
       'Zoya Akhtar', 'Tilak Shetty', 'Rajkumar Santoshi', 'Priyadarshan',
       'Sooraj R. Barjatya', 'Ashutosh Gowariker', 'Milan Luthria'],
      dtype=object)


df_year=df_india_shows.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Shows Released in the Year")
plt.xlabel("Year")
plt.show()


df_year=df_india_movies.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


df_week=df_india_shows.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_week=df_india_movies.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_month=df_india_shows.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("TV Shows Released in the Month")
plt.xlabel("Month")
plt.show()


df_month=df_india_movies.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("Movies Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_india_movies[df_india_movies['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


df_release_year=df_india_shows[df_india_shows['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


#Analysing a combination of actors and directors
df_india_movies['Actor_Director_Combination'] = df_india_movies.Actors.str.cat(df_india_movies.Directors, sep=' and ')
df_india_movies_subset=df_india_movies[df_india_movies['Actors']!='Unknown Actor']
df_india_movies_subset=df_india_movies_subset[df_india_movies_subset['Directors']!='Unknown Director']
df_india_movies_subset.head()


df_india_shows['Actor_Director_Combination'] = df_india_shows.Actors.str.cat(df_india_shows.Directors, sep=' and ')
df_india_shows_subset=df_india_shows[df_india_shows['Actors']!='Unknown Actor']
df_india_shows_subset=df_india_shows_subset[df_india_shows_subset['Directors']!='Unknown Director']
df_india_shows_subset.head()


df_actors_directors=df_india_shows_subset.groupby(['Actor_Director_Combination']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_actors_directors[::-1]['Actor_Director_Combination'], df_actors_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actor-Director Combination')
plt.show()


df_actors_directors=df_india_movies_subset.groupby(['Actor_Director_Combination']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_actors_directors[::-1]['Actor_Director_Combination'], df_actors_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actor-Director Combination')
plt.show()


df_india_movies[df_india_movies['Directors']=='Rajiv Chilaka']


df_actors_directors['Actor_Director_Combination'].values

array(['Rajesh Kava and Rajiv Chilaka', 'Julie Tejwani and Rajiv Chilaka',
       'Rupa Bhimani and Rajiv Chilaka',
       'Jigna Bhardwaj and Rajiv Chilaka',
       'Vatsal Dubey and Rajiv Chilaka', 'Mousam and Rajiv Chilaka',
       'Swapnil and Rajiv Chilaka', 'Saurav Chakraborty and Suhas Kadav',
       'Smita Malhotra and Tilak Shetty', 'Anupam Kher and David Dhawan',
       'Salman Khan and Sooraj R. Barjatya',
       'Alok Nath and Sooraj R. Barjatya',
       'Sonal Kaushal and Prakash Satam',
       'Mithun Chakraborty and Umesh Mehra', 'Mayur Vyas and Suhas Kadav'],
      dtype=object)


#Analyzing India for both shows and movies
df_uk_shows=df_final1[df_final1['country']=='United Kingdom'][df_final1[df_final1['country']=='United Kingdom']['type']=='TV Show']
df_uk_movies=df_final1[df_final1['country']=='United Kingdom'][df_final1[df_final1['country']=='United Kingdom']['type']=='Movie']


df_genre=df_uk_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_genre=df_uk_movies.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_rating=df_uk_shows.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_rating=df_uk_movies.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_duration=df_uk_movies.groupby(['duration']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,8))
plt.barh(df_duration[::-1]['duration'], df_duration[::-1]['title'],color=['pink'])
plt.xlabel('Frequency by Duration')
plt.ylabel('Duration')
plt.show()


df_actors=df_uk_shows.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actors')
plt.show()


df_actors['Actors'].values

array(['David Attenborough', 'Terry Jones', 'Graham Chapman',
       'John Cleese', 'Eric Idle', 'Michael Palin', 'Terry Gilliam',
       'Teresa Gallagher', 'Harriet Walter'], dtype=object)


df_actors=df_uk_movies.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actors')
plt.show()


df_actors['Actors'].values

array(['John Cleese', 'Michael Palin', 'Judi Dench', 'Keith Wickham',
       'Eric Idle', 'Brendan Gleeson', 'Terry Gilliam', 'Terry Jones',
       'Helena Bonham Carter', 'Graham Chapman', 'Samuel West',
       'Eddie Marsan', 'James Cosmo', 'Rob Rackstraw'], dtype=object)


df_directors=df_uk_shows.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_directors['Directors'].values

array(['Alastair Fothergill', 'Jesse Vile', 'Tony Collingwood',
       'Toby Haynes', 'Simon Frederick', 'Rachel Bell',
       'Philippa Lowthorpe', 'Noam Murro', 'Michael Samuels'],
      dtype=object)


df_directors=df_uk_movies.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Directors')
plt.show()


df_directors['Directors'].values

array(['Joey So', 'Edward Cotterill', 'Chris Howe', 'Paul Dugdale',
       'Jerry Rothwell', 'Blair Simmons', 'Tom Hooper', 'Vince Marcello',
       'Terry Gilliam', 'Martin Campbell', 'Orlando von Einsiedel',
       'Benjamin Turner', 'Nick Broomfield', 'Jane Campion'], dtype=object)


df_year=df_uk_movies.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


df_year=df_uk_shows.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


df_week=df_uk_shows.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Shows Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_week=df_uk_movies.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_month=df_uk_shows.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("TV Shows Released in the Month")
plt.xlabel("Month")
plt.show()


df_month=df_uk_movies.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("Movies Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_uk_shows[df_uk_shows['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


df_release_year=df_uk_movies[df_uk_movies['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


#Analysing a combination of actors and directors
df_uk_movies['Actor_Director_Combination'] = df_uk_movies.Actors.str.cat(df_uk_movies.Directors, sep=' and ')
df_uk_movies_subset=df_uk_movies[df_uk_movies['Actors']!='Unknown Actor']
df_uk_movies_subset=df_uk_movies_subset[df_uk_movies_subset['Directors']!='Unknown Director']
df_uk_movies_subset.head()


df_uk_shows['Actor_Director_Combination'] = df_uk_shows.Actors.str.cat(df_uk_shows.Directors, sep=' and ')
df_uk_shows_subset=df_uk_shows[df_uk_shows['Actors']!='Unknown Actor']
df_uk_shows_subset=df_uk_shows_subset[df_uk_shows_subset['Directors']!='Unknown Director']
df_uk_shows_subset.head()


df_actors_directors=df_uk_shows_subset.groupby(['Actor_Director_Combination']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_actors_directors[::-1]['Actor_Director_Combination'], df_actors_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actor-Director Combination')
plt.show()


df_actors_directors=df_uk_movies_subset.groupby(['Actor_Director_Combination']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_actors_directors[::-1]['Actor_Director_Combination'], df_actors_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Movies')
plt.ylabel('Popular Actor-Director Combination')
plt.show()


df_actors_directors['Actor_Director_Combination'].values

array(['Keith Wickham and Joey So', 'Rob Rackstraw and Joey So',
       'Simon Greenall and Blair Simmons',
       'Rachael Stirling and Edward Cotterill',
       'Joey King and Vince Marcello',
       'Teresa Gallagher and Blair Simmons',
       'Paul Panting and Blair Simmons',
       'Molly Ringwald and Vince Marcello',
       'Keith Wickham and Blair Simmons',
       'Michael Murphy and Blair Simmons', 'Jo Wyatt and Blair Simmons',
       'Joel Courtney and Vince Marcello',
       'Jacob Elordi and Vince Marcello',
       'Rob Rackstraw and Blair Simmons', 'Ashley Chin and Femi Oyeniran'],
      dtype=object)


#Analyzing India for both shows and movies
df_japan_shows=df_final1[df_final1['country']=='Japan'][df_final1[df_final1['country']=='Japan']['type']=='TV Show']


df_genre=df_japan_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_rating=df_japan_shows.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_actors=df_japan_shows.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actors')
plt.show()


df_actors['Actors'].values

array(['Takahiro Sakurai', 'Yuki Kaji', 'Daisuke Ono', 'Junichi Suwabe',
       'Ai Kayano', 'Yuichi Nakamura', 'Yoshimasa Hosoya', 'Jun Fukuyama',
       'Hiroshi Kamiya', 'Kana Hanazawa'], dtype=object)


df_directors=df_japan_shows.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Directors')
plt.show()


df_directors['Directors'].values

array(['Caroline Sá', 'Kobun Shizuno', 'Tsutomu Mizushima',
       'Thomas Astruc', 'Tensai Okamura', 'Takuya Igarashi', 'Sion Sono',
       'Moyoung Jin', 'Masaaki Yuasa'], dtype=object)


df_year=df_japan_shows.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


df_week=df_japan_shows.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_month=df_japan_shows.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("TV Shows Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_japan_shows[df_japan_shows['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()


#Analyzing India for both shows and movies
df_sk_shows=df_final1[df_final1['country']=='South Korea'][df_final1[df_final1['country']=='South Korea']['type']=='TV Show']


df_genre=df_sk_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_genre[::-1]['Genre'], df_genre[::-1]['title'],color=['orange'])
plt.xlabel('Frequency of Genres')
plt.ylabel('Genres')
plt.show()


df_rating=df_sk_shows.groupby(['rating']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,8))
plt.barh(df_rating[::-1]['rating'], df_rating[::-1]['title'],color=['violet'])
plt.xlabel('Frequency by Ratings')
plt.ylabel('Ratings')
plt.show()


df_actors=df_sk_shows.groupby(['Actors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_actors=df_actors[df_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,8))
plt.barh(df_actors[::-1]['Actors'], df_actors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Actors')
plt.show()


df_actors['Actors'].values

array(['Sung Dong-il', 'Kim Won-hae', 'Cho Seong-ha', 'Nam Joo-hyuk',
       'Cho Han-cheul', 'Kim Sun-young', 'Kim Min-sang', 'Lee Il-hwa',
       'Oh Jung-se', 'Jang Seung-jo'], dtype=object)


df_directors=df_sk_shows.groupby(['Directors']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:10]
df_directors=df_directors[df_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,8))
plt.barh(df_directors[::-1]['Directors'], df_directors[::-1]['title'],color=['indigo'])
plt.xlabel('Number of Shows')
plt.ylabel('Popular Directors')
plt.show()


df_year=df_sk_shows.groupby(['year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_year, x='year', y='title')
plt.ylabel("Movies Released in the Year")
plt.xlabel("Year")
plt.show()


df_week=df_sk_shows.groupby(['week_Added']).agg({"title":"nunique"}).reset_index()
plt.figure(figsize=(15,8))
sns.lineplot(data=df_week, x='week_Added', y='title')
plt.ylabel("Movies Released in the Week")
plt.xlabel("Week No.")
plt.show()


df_month=df_sk_shows.groupby(['month_added']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_month, x='month_added', y='title')
plt.ylabel("TV Shows Released in the Month")
plt.xlabel("Month")
plt.show()


df_release_year=df_sk_shows[df_sk_shows['release_year']>=1980].groupby(['release_year']).agg({"title":"nunique"}).reset_index()
sns.lineplot(data=df_release_year, x='release_year', y='title')
plt.ylabel("Movies Actual Release Date")
plt.xlabel("Year")
plt.show()

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	September 25, 2021	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	September 24, 2021	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	September 24, 2021	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	September 25, 2021	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	September 24, 2021	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	September 24, 2021	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...
5	s6	TV Show	Midnight Mass	Mike Flanagan	Kate Siegel, Zach Gilford, Hamish Linklater, H...	NaN	September 24, 2021	2021	TV-MA	1 Season	TV Dramas, TV Horror, TV Mysteries	The arrival of a charismatic young priest brin...
6	s7	Movie	My Little Pony: A New Generation	Robert Cullen, José Luis Ucha	Vanessa Hudgens, Kimiko Glenn, James Marsden, ...	NaN	September 24, 2021	2021	PG	91 min	Children & Family Movies	Equestria's divided. But a bright-eyed hero be...
7	s8	Movie	Sankofa	Haile Gerima	Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D...	United States, Ghana, Burkina Faso, United Kin...	September 24, 2021	1993	TV-MA	125 min	Dramas, Independent Movies, International Movies	On a photo shoot in Ghana, an American model s...
8	s9	TV Show	The Great British Baking Show	Andy Devonshire	Mel Giedroyc, Sue Perkins, Mary Berry, Paul Ho...	United Kingdom	September 24, 2021	2021	TV-14	9 Seasons	British TV Shows, Reality TV	A talented batch of amateur bakers face off in...
9	s10	Movie	The Starling	Theodore Melfi	Melissa McCarthy, Chris O'Dowd, Kevin Kline, T...	United States	September 24, 2021	2021	PG-13	104 min	Comedies, Dramas	A woman adjusting to life after a loss contend...

	title	Directors
0	Dick Johnson Is Dead	Kirsten Johnson
1	Blood & Water	nan
2	Ganglands	Julien Leclercq
3	Jailbirds New Orleans	nan
4	Kota Factory	nan

	title	Actors
0	Dick Johnson Is Dead	nan
1	Blood & Water	Ama Qamata
2	Blood & Water	Khosi Ngema
3	Blood & Water	Gail Mabalane
4	Blood & Water	Thabang Molaba

	title	Genre
0	Dick Johnson Is Dead	Documentaries
1	Blood & Water	International TV Shows
2	Blood & Water	TV Dramas
3	Blood & Water	TV Mysteries
4	Ganglands	Crime TV Shows

Univariate Analysis in terms of counts of each column¶

Univariate Analysis separately for shows and movies¶

Questions to be Explored Now for Recommendations¶

Univariate Analysis separately for shows and movies in USA¶

Univariate Analysis separately for shows and movies in India¶

Univariate Analysis separately for shows and movies in United Kingdom¶

Univariate Analysis separately for shows in Japan¶

Univariate Analysis separately for shows in South Korea¶

Recommendations¶

	title	Actors	Directors	Genre	country
0	Dick Johnson Is Dead	Unknown Actor	Kirsten Johnson	Documentaries	United States
1	Blood & Water	Ama Qamata	Unknown Director	International TV Shows	South Africa
2	Blood & Water	Ama Qamata	Unknown Director	TV Dramas	South Africa
3	Blood & Water	Ama Qamata	Unknown Director	TV Mysteries	South Africa
4	Blood & Water	Khosi Ngema	Unknown Director	International TV Shows	South Africa

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration
136893	A Young Doctor's Notebook and Other Stories	Daniel Radcliffe	Unknown Director	British TV Shows	United Kingdom	s6067	TV Show	NaN	2013	TV-MA	2 Seasons
136894	A Young Doctor's Notebook and Other Stories	Daniel Radcliffe	Unknown Director	TV Comedies	United Kingdom	s6067	TV Show	NaN	2013	TV-MA	2 Seasons
136895	A Young Doctor's Notebook and Other Stories	Daniel Radcliffe	Unknown Director	TV Dramas	United Kingdom	s6067	TV Show	NaN	2013	TV-MA	2 Seasons
136896	A Young Doctor's Notebook and Other Stories	Jon Hamm	Unknown Director	British TV Shows	United Kingdom	s6067	TV Show	NaN	2013	TV-MA	2 Seasons
136897	A Young Doctor's Notebook and Other Stories	Jon Hamm	Unknown Director	TV Comedies	United Kingdom	s6067	TV Show	NaN	2013	TV-MA	2 Seasons

	title
Genre
International Movies	2738
Dramas	2418
Comedies	1673
International TV Shows	1351
Documentaries	869
Action & Adventure	854
TV Dramas	763
Independent Movies	756
Children & Family Movies	639
Romantic Movies	615
TV Comedies	581
Thrillers	573
Crime TV Shows	470
Kids' TV	451
Docuseries	395
Music & Musicals	372
Romantic TV Shows	370
Horror Movies	353
Stand-Up Comedy	343
Reality TV	255
British TV Shows	253
Sci-Fi & Fantasy	243
Sports Movies	219
Anime Series	176
Spanish-Language TV Shows	174
TV Action & Adventure	168
Korean TV Shows	151
Classic Movies	116
LGBTQ Movies	102
TV Mysteries	98
Science & Nature TV	92
TV Sci-Fi & Fantasy	84
TV Horror	75
Anime Features	71
Cult Movies	71
Teen TV Shows	69
Faith & Spirituality	65
TV Thrillers	57
Movies	57
Stand-Up Comedy & Talk Shows	56
Classic & Cult TV	28
TV Shows	16

	title
country
	3
Afghanistan	1
Albania	1
Algeria	3
Angola	2
Argentina	94
Armenia	1
Australia	162
Austria	12
Azerbaijan	1
Bahamas	1
Bangladesh	4
Belarus	1
Belgium	94
Bermuda	1
Botswana	1
Brazil	103
Bulgaria	10
Burkina Faso	1
Cambodia	5
Cambodia,	1
Cameroon	2
Canada	460
Cayman Islands	2
Chile	30
China	166
Colombia	54
Croatia	4
Cuba	2
Cyprus	1
Czech Republic	23
Denmark	50
Dominican Republic	1
East Germany	1
Ecuador	1
Egypt	134
Ethiopia	1
Finland	12
France	409
Georgia	2
Germany	231
Ghana	8
Greece	11
Guatemala	2
Hong Kong	110
Hungary	11
Iceland	11
India	1126
Indonesia	97
Iran	4
Iraq	2
Ireland	46
Israel	30
Italy	102
Jamaica	1
Japan	338
Jordan	10
Kazakhstan	1
Kenya	6
Kuwait	9
Latvia	1
Lebanon	33
Liechtenstein	1
Lithuania	1
Luxembourg	12
Malawi	1
Malaysia	26
Malta	3
Mauritius	3
Mexico	175
Mongolia	1
Montenegro	1
Morocco	6
Mozambique	1
Namibia	2
Nepal	2
Netherlands	50
New Zealand	33
Nicaragua	1
Nigeria	140
Norway	30
Pakistan	24
Palestine	1
Panama	1
Paraguay	1
Peru	11
Philippines	90
Poland	41
Poland,	1
Portugal	6
Puerto Rico	1
Qatar	10
Romania	14
Russia	27
Samoa	1
Saudi Arabia	14
Senegal	3
Serbia	7
Singapore	41
Slovakia	1
Slovenia	3
Somalia	1
South Africa	65
South Korea	235
Soviet Union	3
Spain	239
Sri Lanka	1
Sudan	1
Sweden	44
Switzerland	19
Syria	3
Taiwan	94
Thailand	74
Turkey	115
Uganda	1
Ukraine	3
United Arab Emirates	38
United Kingdom	829
United Kingdom,	2
United States	4245
United States,	1
Unknown Country	175
Uruguay	14
Vatican City	1
Venezuela	4
Vietnam	7
West Germany	5
Zimbabwe	3

	title
rating
G	41
NC-17	3
NR	87
PG	287
PG-13	490
R	799
TV-14	2151
TV-G	220
TV-MA	3204
TV-PG	863
TV-Y	305
TV-Y7	334
TV-Y7-FV	6
UR	3

	title
duration
1 Season	1793
1-50	287
10 Seasons	7
100-120	1671
11 Seasons	2
12 Seasons	2
120-150	891
13 Seasons	3
15 Seasons	2
150-200	222
17 Seasons	1
2 Seasons	425
200-315	19
3 Seasons	199
4 Seasons	95
5 Seasons	65
50-80	808
6 Seasons	33
7 Seasons	23
8 Seasons	17
80-100	2220
9 Seasons	9

	title
year
2008	2
2009	2
2010	1
2011	13
2012	3
2013	11
2014	24
2015	82
2016	432
2017	1185
2018	1650
2019	2012
2020	1877
2021	1498

	title
week_Added
1	372
2	108
3	113
4	88
5	208
6	97
7	147
8	110
9	254
10	135
11	163
12	109
13	250
14	173
15	153
16	160
17	154
18	234
19	116
20	130
21	117
22	206
23	151
24	164
25	143
26	269
27	241
28	131
29	140
30	160
31	269
32	118
33	153
34	139
35	265
36	142
37	183
38	139
39	165
40	287
41	116
42	133
43	116
44	318
45	98
46	134
47	120
48	200
49	140
50	189
51	137
52	132
53	104

	title
month_added
1	738
2	563
3	742
4	765
5	631
6	726
7	832
8	748
9	771
10	758
11	706
12	813

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration	Modified_Added_date	month_added	week_Added	year	Actor_Director_Combination
159	My Little Pony: A New Generation	Vanessa Hudgens	Robert Cullen	Children & Family Movies	United States	s7	Movie	September 24, 2021	2021	PG	80-100	2021-09-24	9	38	2021	Vanessa Hudgens and Robert Cullen
160	My Little Pony: A New Generation	Vanessa Hudgens	José Luis Ucha	Children & Family Movies	United States	s7	Movie	September 24, 2021	2021	PG	80-100	2021-09-24	9	38	2021	Vanessa Hudgens and José Luis Ucha
161	My Little Pony: A New Generation	Kimiko Glenn	Robert Cullen	Children & Family Movies	United States	s7	Movie	September 24, 2021	2021	PG	80-100	2021-09-24	9	38	2021	Kimiko Glenn and Robert Cullen
162	My Little Pony: A New Generation	Kimiko Glenn	José Luis Ucha	Children & Family Movies	United States	s7	Movie	September 24, 2021	2021	PG	80-100	2021-09-24	9	38	2021	Kimiko Glenn and José Luis Ucha
163	My Little Pony: A New Generation	James Marsden	Robert Cullen	Children & Family Movies	United States	s7	Movie	September 24, 2021	2021	PG	80-100	2021-09-24	9	38	2021	James Marsden and Robert Cullen

	title
Actors
Jr.	2
"Riley" Lakdhar Dridi	1
'Najite Dede	2
2 Chainz	1
2Mex	1
4Minute	1
50 Cent	5
9m88	1
A Boogie Wit tha Hoodie	1
A. Murat Özgen	1
A.C. Peterson	1
A.D. Miles	3
A.J. Cook	2
A.J. Johnson	1
A.J. LoCascio	3
A.K. Hangal	4
A.R. Rahman	1
A.S. Sasi Kumar	1
AC Lim	1
AFRA	1
AJ Bowen	1
AJ Michalka	1
AJ Rivera	1
ARAH	2
Aabhas Yadav	1
Aachal Munjal	1
Aadarsh Balakrishna	2
Aadhi	1
Aadhya Anand	1
Aadil Khan	1
Aaditi Pohankar	2
Aaditya Pratap Singh	1
Aadukalam Naren	1
Aadya Bedi	1
Aahana Kumra	2
Aakarshan Singh	1
Aakash Dabhade	4
Aakash Dahiya	1
Aakash Pandey	1

	title
Directors
A. L. Vijay	2
A. Raajdheep	1
A. Salaam	1
A.R. Murugadoss	2
Aadish Keluskar	1
Aamir Bashir	1
Aamir Khan	1
Aanand Rai	1
Aaron Burns	1
Aaron Hancox	1
Aaron Hann	1
Aaron Lieber	1
Aaron Moorhead	2
Aaron Nee	1
Aaron Sorkin	2
Aaron Woodley	1
Aaron Woolf	1
Aatmaram Dharne	1
Abba T. Makama	2
Abbas Alibhai Burmawalla	5
Abbas Mustan	1
Abbas Tyrewala	1
Abby Epstein	1
Abdellatif Kechiche	1
Abdul Aziz Hashad	1
Abdulaziz Alshlahei	1
Abdullah Al Noor	1
Abel Ferrara	1
Abhay Chopra	1
Abhijeet Deshpande	1
Abhijit Kokate	1
Abhijit Panse	1
Abhinav Shiv Tiwari	1
Abhinay Deo	3
Abhishek Chaubey	5
Abhishek Kapoor	3
Abhishek Saxena	1
Abhishek Sharma	3
Abhishek Varman	1

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration	Modified_Added_date	month_added	week_Added	year	Actor_Director_Combination
621	Avvai Shanmughi	Kamal Hassan	K.S. Ravikumar	Comedies	India	s23	Movie	September 21, 2021	1996	TV-PG	150-200	2021-09-21	9	38	2021	Kamal Hassan and K.S. Ravikumar
622	Avvai Shanmughi	Kamal Hassan	K.S. Ravikumar	International Movies	India	s23	Movie	September 21, 2021	1996	TV-PG	150-200	2021-09-21	9	38	2021	Kamal Hassan and K.S. Ravikumar
629	Avvai Shanmughi	Nassar	K.S. Ravikumar	Comedies	India	s23	Movie	September 21, 2021	1996	TV-PG	150-200	2021-09-21	9	38	2021	Nassar and K.S. Ravikumar
630	Avvai Shanmughi	Nassar	K.S. Ravikumar	International Movies	India	s23	Movie	September 21, 2021	1996	TV-PG	150-200	2021-09-21	9	38	2021	Nassar and K.S. Ravikumar
631	Avvai Shanmughi	S.P. Balasubrahmanyam	K.S. Ravikumar	Comedies	India	s23	Movie	September 21, 2021	1996	TV-PG	150-200	2021-09-21	9	38	2021	S.P. Balasubrahmanyam and K.S. Ravikumar

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration	Modified_Added_date	month_added	week_Added	year	Actor_Director_Combination
111	Midnight Mass	Kate Siegel	Mike Flanagan	TV Dramas	United States	s6	TV Show	September 24, 2021	2021	TV-MA	1 Season	2021-09-24	9	38	2021	Kate Siegel and Mike Flanagan
112	Midnight Mass	Kate Siegel	Mike Flanagan	TV Horror	United States	s6	TV Show	September 24, 2021	2021	TV-MA	1 Season	2021-09-24	9	38	2021	Kate Siegel and Mike Flanagan
113	Midnight Mass	Kate Siegel	Mike Flanagan	TV Mysteries	United States	s6	TV Show	September 24, 2021	2021	TV-MA	1 Season	2021-09-24	9	38	2021	Kate Siegel and Mike Flanagan
114	Midnight Mass	Zach Gilford	Mike Flanagan	TV Dramas	United States	s6	TV Show	September 24, 2021	2021	TV-MA	1 Season	2021-09-24	9	38	2021	Zach Gilford and Mike Flanagan
115	Midnight Mass	Zach Gilford	Mike Flanagan	TV Horror	United States	s6	TV Show	September 24, 2021	2021	TV-MA	1 Season	2021-09-24	9	38	2021	Zach Gilford and Mike Flanagan

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration	Modified_Added_date	month_added	week_Added	year	Actor_Director_Combination
7005	Navarasa	Suriya	Bejoy Nambiar	TV Shows	India	s298	TV Show	August 6, 2021	2021	TV-MA	1 Season	2021-08-06	8	31	2021	Suriya and Bejoy Nambiar
7006	Navarasa	Suriya	Priyadarshan	TV Shows	India	s298	TV Show	August 6, 2021	2021	TV-MA	1 Season	2021-08-06	8	31	2021	Suriya and Priyadarshan
7007	Navarasa	Suriya	Karthik Narain	TV Shows	India	s298	TV Show	August 6, 2021	2021	TV-MA	1 Season	2021-08-06	8	31	2021	Suriya and Karthik Narain
7008	Navarasa	Suriya	Vasanth Sai	TV Shows	India	s298	TV Show	August 6, 2021	2021	TV-MA	1 Season	2021-08-06	8	31	2021	Suriya and Vasanth Sai
7009	Navarasa	Suriya	Karthik Subbaraj	TV Shows	India	s298	TV Show	August 6, 2021	2021	TV-MA	1 Season	2021-08-06	8	31	2021	Suriya and Karthik Subbaraj

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration	Modified_Added_date	month_added	week_Added	year	Actor_Director_Combination
182	Sankofa	Kofi Ghanaba	Haile Gerima	Dramas	United Kingdom	s8	Movie	September 24, 2021	1993	TV-MA	120-150	2021-09-24	9	38	2021	Kofi Ghanaba and Haile Gerima
188	Sankofa	Kofi Ghanaba	Haile Gerima	Independent Movies	United Kingdom	s8	Movie	September 24, 2021	1993	TV-MA	120-150	2021-09-24	9	38	2021	Kofi Ghanaba and Haile Gerima
194	Sankofa	Kofi Ghanaba	Haile Gerima	International Movies	United Kingdom	s8	Movie	September 24, 2021	1993	TV-MA	120-150	2021-09-24	9	38	2021	Kofi Ghanaba and Haile Gerima
200	Sankofa	Oyafunmike Ogunlano	Haile Gerima	Dramas	United Kingdom	s8	Movie	September 24, 2021	1993	TV-MA	120-150	2021-09-24	9	38	2021	Oyafunmike Ogunlano and Haile Gerima
206	Sankofa	Oyafunmike Ogunlano	Haile Gerima	Independent Movies	United Kingdom	s8	Movie	September 24, 2021	1993	TV-MA	120-150	2021-09-24	9	38	2021	Oyafunmike Ogunlano and Haile Gerima

	title	Actors	Directors	Genre	country	show_id	type	date_added	release_year	rating	duration	Modified_Added_date	month_added	week_Added	year	Actor_Director_Combination
323	The Great British Baking Show	Mel Giedroyc	Andy Devonshire	British TV Shows	United Kingdom	s9	TV Show	September 24, 2021	2021	TV-14	9 Seasons	2021-09-24	9	38	2021	Mel Giedroyc and Andy Devonshire
324	The Great British Baking Show	Mel Giedroyc	Andy Devonshire	Reality TV	United Kingdom	s9	TV Show	September 24, 2021	2021	TV-14	9 Seasons	2021-09-24	9	38	2021	Mel Giedroyc and Andy Devonshire
325	The Great British Baking Show	Sue Perkins	Andy Devonshire	British TV Shows	United Kingdom	s9	TV Show	September 24, 2021	2021	TV-14	9 Seasons	2021-09-24	9	38	2021	Sue Perkins and Andy Devonshire
326	The Great British Baking Show	Sue Perkins	Andy Devonshire	Reality TV	United Kingdom	s9	TV Show	September 24, 2021	2021	TV-14	9 Seasons	2021-09-24	9	38	2021	Sue Perkins and Andy Devonshire
327	The Great British Baking Show	Mary Berry	Andy Devonshire	British TV Shows	United Kingdom	s9	TV Show	September 24, 2021	2021	TV-14	9 Seasons	2021-09-24	9	38	2021	Mary Berry and Andy Devonshire